Sign on
ADS Classic is now deprecated. It will be completely retired in October 2019. This page will automatically redirect to the new ADS interface at that point.

SAO/NASA ADS Astronomy Abstract Service


· Find Similar Abstracts (with default settings below)
· Electronic Refereed Journal Article (HTML)
· Full Refereed Journal Article (PDF/Postscript)
· References in the article
·
· Translate This Page
Title:
Overview Feature Selection using Fish Swarm Algorithm
Authors:
Rosely, Nur Fatin Liyana Mohd; Salleh@Sallehuddin, Roselina; Zain, Azlan Mohd
Affiliation:
AA(Applied Industrial Analytics Research Group (ALIAS), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Darul Takzim, Malaysia.), AB(Applied Industrial Analytics Research Group (ALIAS), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Darul Takzim, Malaysia.), AC(Applied Industrial Analytics Research Group (ALIAS), School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Darul Takzim, Malaysia.)
Publication:
Journal of Physics: Conference Series, Volume 1192, Issue 1, article id. 012068 (2019).
Publication Date:
08/2019
Origin:
IOP
DOI:
10.1088/1742-6596/1192/1/012068
Bibliographic Code:
2019JPlPh..85d9007H

Abstract

Feature selection is a process of representing wanted features based on the requirement needed by selecting the best subset of a dataset without changing the originality of the dataset. The aim of feature selection is to obtain most optimal feature subset to represent the data and for that purpose feature selection offered a few methods. This paper gives an easy understanding of the feature selection concept and the available methods in feature selection. As nowadays metaheuristics is catching attention researchers in many fields and feature selection is one of them, this paper intentionally brief feature selection using metaheuristics that implement Fish Swarm Algorithm (FSA) in the feature selection process. FSA classified as one of the Swarm Intelligence (SI) techniques have several advantages mainly to solve optimization problems. A number of previous works are reviewed. Based on the reviewed and the outcome results that has been tested using high dimensional, real-valued benchmark data sets, FSA reflect good performance among others SI.
Bibtex entry for this abstract   Preferred format for this abstract (see Preferences)


Find Similar Abstracts:

Use: Authors
Title
Abstract Text
Return: Query Results Return    items starting with number
Query Form
Database: Astronomy
Physics
arXiv e-prints